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dc.contributor.authorMadani Bezoui
dc.contributor.authorAbdelfatah Kermali
dc.contributor.authorAhcène Bounceur
dc.contributor.authorMian Qaisar, Saeed
dc.date.accessioned2024-03-03T05:10:07Z
dc.date.available2024-03-03T05:10:07Z
dc.date.issued2023-11-28
dc.identifier.doihttps://hal.science/hal-04389027/en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/1468
dc.description.abstractIn modern-day manufacturing, it is imperative to react promptly to altering market requirements. Reconfigurable Manufacturing Systems (RMS) are a significant leap forward in achieving this criteria as they offer a flexible and affordable structure to comply with evolving production necessities. The ever-changing nature of RMS demands a sturdy induction of learning algorithms to persistently improve system configurations and scheduling. This study suggests that using Reinforcement Learning (RL), specifically, the Double Deep Q-Network (DDQN) algorithm, is a feasible way to navigate the intricate, multi-objective optimization landscape of RMS. Key points to consider regarding this study include cutting down tardiness costs, ensuring sustainability by reducing wasted liquid and gas emissions during production, optimizing makespan, and improving ergonomics by reducing operator intervention during system reconfiguration. Our proposal consists of two layers. Initially, we suggest a hierarchical and modular architecture for RMS which includes a multi-agent environment at the reconfigurable machine tool level, which improves agent interaction for optimal global results. Secondly, we incorporate DDQN to navigate the multi-objective space in a clever manner, resulting in more efficient and ergonomic reconfiguration and scheduling. The findings indicate that employing RL can help solve intricate optimization issues that come with contemporary manufacturing paradigms, clearing the path for Industry 5.0.en_US
dc.publisherHAL open scienceen_US
dc.subjectSustainabilityen_US
dc.subjectDeep Reinforcement Learningen_US
dc.subjectMultiobjective Schedulingen_US
dc.subjectIndustry 5.0en_US
dc.titleDeep Reinforcement Learning for multiobjective Scheduling in Industry 5.0 Reconfigurable Manufacturing Systems⋆en_US
refterms.dateFOA2024-03-03T05:10:09Z
dc.contributor.researcherExternal Collaborationen_US
dc.contributor.labNAen_US
dc.subject.KSAICTen_US
dc.contributor.ugstudentNAen_US
dc.contributor.alumnaeNAen_US
dc.source.indexScopusen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.contributor.pgstudentNAen_US
dc.contributor.firstauthorMadani Bezoui
dc.conference.locationParis, Franceen_US
dc.conference.name6th International Conference on Machine Learning for Networkingen_US
dc.conference.date2023-11-28


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